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Next Item Recommendation with Self-Attention
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In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user's transient interests. The model is finally trained in a metric learning framework, taking both short-term and long-term intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin.
Forward citations
Cited by 3 Pith papers
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SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
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Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
An unsupervised multi-relational GCN learner-modeling pipeline is described, but its own user study finds no significant benefit over the single-relation ConceptGCN baseline.
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Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information
Adding VGG-19 style embeddings and shopping cart training sessions to a transformer recommender raised HR@5 from 0.681 to 0.735 on a proprietary e-commerce dataset.
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